DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Improve Cardiac Emergency Preparedness by Building a Team-Based Cardiopulmonary Resuscitation Educational Plan.

Objective: To design an innovative team-based cardiopulmonary resuscitation (CPR) educational plan for multiple bystanders and evaluate whether it was associated with better teamwork and higher quality of resuscitation. Methods: The team-based CPR plan defined the process for a three-person team, emphasize task allocation, leadership, and closed-loop communication. Participants qualified for single-rescuer CPR skills were randomized into teams of 3. The teamwork performance and CPR operation skills were evaluated in one simulated cardiac arrest scenario before and after training on the team-based CPR plan. The primary outcomes were measured by the Team Emergency Assessment Measure (TEAM) scale and chest compression fraction (CCF). Results: Forty-three teams were included in the analysis. The team-based CPR plan significantly improved the team performance (global rating 6.7 ± 1.3 vs. 9.0 ± 0.7, corrected p < 0.001 after Bonferroni's correction). After implementing the team-based CPR plan, CCF increased [median 59 (IQR 48-69) vs. 64 (IQR 57-71%)%, corrected p = 0.002], while hands-off time decreased [median 233.2 (IQR 181.0-264.0) vs. 207 (IQR 174-222.9) s, corrected p = 0.02]. We found the average compression depth was significantly improved through the team-based CPR training [median 5.1 (IQR 4.7-5.6) vs. 5.3 (IQR 4.9-5.5) cm, p = 0.03] but no more significantly after applying the Bonferroni's correction (corrected p = 0.35). The compression depths were significantly improved by collaborating and exchanging the role of compression among the participants after the 6th min. Conclusion: The team-based CPR plan is feasible for improving bystanders teamwork performance and effective for improving resuscitation quality in prearrival care. We suggest a wide application of the team-based CPR plan in the educational program for better resuscitation performance in real rescue events.